Abstract—This contribution discusses a novel many-objective optimization algorithm that combines an ant colony optimization based decomposition approach with a massive parallelization framework. A rigorous numerical analysis on the impact of the two varying key factors of the here considered parallelization approach is presented. Those factors are the number of co-evaluated solution candidates within an individual ant colony algorithm and the number of individual ant colony algorithms itself. Aim of the presented method is to solve a many-objective application corresponding to the interplanetary space trajectory of the Cassini probe, launched by NASA in 1997. The provided numerical results indicate that comprehensive mission analysis via a many-objective approach is possible and that the presented approach is highly suitable for massive parallelization.